Using mobile data to track urban contagion

Everyday travel within a city especially commuting is an important factor influencing the spread of certain diseases in urban settings now researchers using mobile data to track urban contagion.

The study, co-authored by MIT researchers, uses aggregated, now using mobile data to track urban contagion and the spread of dengue, a mosquito-borne virus, in Singapore during 2013 and 2014.

While many studies have linked human travel to the spread of disease over long distances, the current finding is notable for its granularity, tracking the path of contagion over shorter distances and times.

Future research will needed to determine whether the findings also apply to airborne diseases, such as the flu.

Human mobility is an important factor in the vector-borne disease epidemics at the urban scales.

To conduct the study, the researchers examined how different models of human movement fit with the spread of dengue fever in Singapore during two outbreaks, in 2013 and 2014.

Dengue fever transmitted from mosquitoes to people; there are an estimated 50 million human infections around the world, leading to about 500,000 hospitalizations and 25,000 deaths each year.

Given health data about the spread of dengue fever spread in Singapore, the researchers created four models of human mobility in the city-state during this time.

The models used data about the estimated number of mosquitoes per human and the mosquito bite rate.

The researchers then evaluated which model of mobility corresponded best with the propagation of the illness.

The first model used anonymized call records for 2.3 million people in Singapore from 2011, showing a typical pattern of population movement; the second model, by contrast.

Given the numbers of mosquitoes and the infection rates, the scholars found that two of these four models the one using mobile phone data, and the radiation model performed the best, approximating the spatial distribution of dengue cases that had occurred, over time, during the outbreaks.

The researchers adopted a metric used in image processing to quantify the difference between the simulated snapshots of infected cases and the real results; a model would score 1 if the two maps of infections were the same, and 0 if they had nothing in common.

The models and real outcomes compared every week.

Averaged over time, the two best models each scored below 0.8; the model with the probability distribution of movement scored under 0.7; and the model with random movement performed worst, under 0.6.

To be clear, dengue fever comes from mosquitoes themselves and is not transmitted face-to-face among people.

The higher risk of contagion, thus, could occur due to more people entering into mosquito-infested areas, for example during their work commutes.

We expect that the spread of such diseases like the flu will have a different pattern, and it is an interesting question to what extent the current results apply their researcher says, noting that future studies could explore this point with similar levels of detail.

The researcher believes this kind of research could prove especially important in developing countries.

The current study can extend into a further examination of the precise options officials would have available to them, in case of future outbreaks.

In the next steps, we would aim at developing models and data-driven monitoring systems to detect the safest commuting urban pathways, the researcher says.


The study was published in Massachusetts Institute of Technology

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